Acceptance sampling for cost-effective surveillance of emerald ash borer in urban environments
Bibliographic record
Abstract
We develop an acceptance sampling approach for surveillance of the emerald ash borer (EAB), a harmful forest pest, in Winnipeg, Canada. We compare sampling strategies computed with two different management objectives. The first objective maximizes the expected area with detected infestations and the second objective minimizes the expected number of undetected infested trees in sites that were not inspected or where inspection did not find an infestation. The choice of the management objective influences the survey strategy: achieving the first objective involves selecting sites with high infestation rates proximal to the infested area, whereas the second objective requires inspecting sites with both high infestation rates and high host densities. Adding uncertainty prescribes inspecting a larger area with lower sampling rates and extending the surveys to farther distances from the infested locations. If a decision maker wants to minimize the worst-case damage from failed detections, the optimal strategy is to survey more sites with high host densities at farther distances, where EAB arrivals could cause significant damage if not detected quickly. Accounting for the uncertainty addresses possible variation in infestation rates and helps develop a more diversified survey strategy. The approach is generalizable and can support survey programmes for new pest incursions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".